Analysing livestock farm odour using an adaptive neuro-fuzzy approach

In livestock farming, odour measurement and reduction are necessary for a cleaner environment, lower health risks to humans, and higher quality of livestock production. There have been many studies on modelling of livestock farm odour by analysing the chemical components in odorous air. It is suggested that the component analysis approaches should be extended to factors such as temperature, relative humidity, and airflow speed. In this paper, a neuro-fuzzy-based method for analysing odour generation factors to the perception of livestock farm odour was proposed. The proposed approach incorporates neuro-adaptive learning techniques into fuzzy logic method. Rather than choosing the parameters associated with a given membership function by trail and error, these parameters could be tuned automatically in a systematic manner so as to adjust the membership functions of the input/output variables for optimal system performance. A multi-factor livestock farm odour model was developed, and both numeric factors and linguistic factors were considered. The proposed approach was tested with a livestock farm odour database. The results demonstrated the effectiveness of the proposed approach in comparison to a typical neural network model.